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Fundamental

AI · San Francisco, United States · Founded 2024

AI-powered predictive intelligence from large tabular enterprise data MoreLess

Fundamental is an AI company developing predictive intelligence for enterprise data. The firm was established in October 2024 by alumni from DeepMind, with Jeremy Fraenkel as the CEO and co-founder. Emerging from stealth on February 5, 2026, the company announced it had secured $255 million in funding, consisting of a $30 million Seed round and a $225 million Series A round. The Series A was led by Oak HC/FT, with participation from Valor Equity Partners, Battery Ventures, Salesforce Ventures, and Hetz Ventures.

The company's core product is NEXUS, a Large Tabular Model (LTM) purpose-built to analyze structured business data found in spreadsheets, databases, and CRM records. Unlike Large Language Models (LLMs) that focus on unstructured text and images, NEXUS is designed to derive insights from the billions of tables that underpin corporate decision-making. The model ingests raw tabular data and automatically learns underlying patterns and dependencies without requiring extensive manual feature engineering. This allows enterprises to move beyond historical analysis to forecast future events, identify risks, and uncover opportunities. NEXUS unifies various analytics tasks, including forecasting, classification, anomaly detection, and scenario simulation, into a single foundation model.

Fundamental targets enterprise clients across numerous verticals, including financial services, healthcare, retail, and energy. Before its public launch, the company had already secured seven-figure contracts with Fortune 100 companies for use cases such as demand forecasting, price prediction, and churn analysis. The business model centers on providing access to NEXUS, which is available to enterprises and can be deployed directly through the AWS dashboard via a strategic partnership. The model is built to handle enterprise-scale datasets with billions of rows and was trained on Amazon SageMaker HyperPod.

Keywords: Large Tabular Model, LTM, enterprise AI, structured data, predictive intelligence, data forecasting, tabular data analysis, DeepMind alumni, NEXUS AI, business decision automation, churn analysis, demand forecasting, price prediction, financial fraud detection, risk prediction, enterprise software, data analytics

Talent graph

Fundamental's talent

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Team profiled 9 named & role-tagged
Founders Hidden — book a demo/100 quality score
Key operators 3 quality Hidden — book a demo/100
Potential founders Hidden — book a demo likely future founders
Startup mafia Hidden — book a demo alumni-founded startups
Talent movements Hidden — book a demo in · Hidden — book a demo out past 12 months

Source: Dealroom Talent Intelligence.

Investors

14 investors on Fundamental's cap table

Sourced from Dealroom's funding history. Investor profile links are shown where available.

Seed 7 investors entered at this stage
Olivier Pomel
Quadrille
Henrique Dubugras
Mandeep Singh
Kima Ventures
Aravind Srinivas
Drysdale Ventures
Series A 7 investors entered at this stage
Battery Ventures
Valor Equity Partners
Salesforce Ventures
Oak HC/FT
Hetz Ventures
Motier Ventures
MUFG Innovation Partners

Global footprint

Where Fundamental has talent and traffic

Workforce by country
7 countries with
team presence
🇺🇸 United States51.1%
🇪🇸 Spain21.3%
🇮🇱 Israel14.9%
🇬🇧 United Kingdom6.4%
🇳🇱 Netherlands2.1%
Top 5 of 7 shown
Web traffic by country
19K monthly visits
across markets
🇺🇸 United States45.3%
🇩🇪 Germany14.2%
🇫🇷 France8.9%
🇬🇧 United Kingdom8.9%
🇮🇱 Israel5.8%
Top 5 markets shown

Market sentiment

What the market is saying about Fundamental

An AI-synthesized read of the highest-engagement posts about Fundamental on X over the past 7 days. We rank by likes & retweets, ignore corporate channels, and surface the themes that broke out from real people.

Reading the room on X — pulling top posts and synthesizing themes…

Source: X recent search ranked by engagement (likes + retweets) · Synthesis by Claude · Cached for 1 hour

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